Correcting the Cognitive Bias for Commuting Time to Relieve the Driving Stress Level in Snow Weather Condition: A Naturalistic Driving Study in Harbin, China
Bibliographic record
Abstract
As a negative emotion, professional drivers’ stress levels significantly affected driving behavior and thus were related to driving safety issues. Nevertheless, current evidence fell considerably short of explaining whether and why private drivers’ stress levels might be influenced while commuting driving in a specific scenario and how to relieve their stress levels. This study aimed to identify and analyze the contributing factors of the drivers’ stress levels while commuting driving in various scenarios (clear or snow weather conditions). On weekdays between 1st October 2020 and 31st January 2022, the questionnaire data from a sample of 985 private drivers were collected from six different locations of business districts in Harbin, China. Based on the naturalistic driving study (NDS) database, a 7‐item questionnaire was designed for participants to self‐report their driving stress levels in various scenarios, which was generated from the shortened and adapted version of the Perceived Stress Scale (PSS). The results showed that participants’ stress levels had significantly increased in snow weather conditions, especially nervous and stressed feeling, and unable to control the arrival time, which indicated that participants’ highly increased cognitive bias for commuting time could be the critical reason. The results of hierarchical linear regression models indicated that overall stress scores could be predicted through participants’ sociodemographic characteristics, driving experience, commuting driving, and cognitive bias for commuting time. Such an association was significantly strongest with commuting time gaps, especially in snow weather conditions. In addition, a recommendation was derived from these results that correcting the cognitive bias for commuting time could relieve participants’ stress levels. The implication of the reminder message supported this recommendation. The participants’ stress levels were reduced significantly after providing a reminder message every 10 mins while commuting driving in clear weather conditions and every 5 mins in snow weather conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".